Evolution- and Structure-Based Computational Strategy Reveals the Impact of Deleterious Missense Mutations on MODY 2 (Maturity-Onset Diabetes of the Young, Type 2)

Abstract

Heterozygous mutations in the central glycolytic enzyme glucokinase (GCK) can result in an autosomal dominant inherited disease, namely maturity-onset diabetes of the young, type 2 (MODY 2). MODY 2 is characterised by early onset: it usually appears before 25 years of age and presents as a mild form of hyperglycaemia. In recent years, the number of known GCK mutations has markedly increased. As a result, interpreting which mutations cause a disease or confer susceptibility to a disease and characterising these deleterious mutations can be a difficult task in large-scale analyses and may be impossible when using a structural perspective. The laborious and time-consuming nature of the experimental analysis led us to attempt to develop a cost-effective computational pipeline for diabetic research that is based on the fundamentals of protein biophysics and that facilitates our understanding of the relationship between phenotypic effects and evolutionary processes. In this study, we investigate missense mutations in the GCK gene by using a wide array of evolution- and structure-based computational methods, such as SIFT, PolyPhen2, PhD-SNP, SNAP, SNPs&GO, fathmm, and Align GVGD. Based on the computational prediction scores obtained using these methods, three mutations, namely E70K, A188T, and W257R, were identified as highly deleterious on the basis of their effects on protein structure and function. Using the evolutionary conservation predictors Consurf and Scorecons, we further demonstrated that most of the predicted deleterious mutations, including E70K, A188T, and W257R, occur in highly conserved regions of GCK. The effects of the mutations on protein stability were computed using PoPMusic 2.1, I-mutant 3.0, and Dmutant. We also conducted molecular dynamics (MD) simulation analysis through in silico modelling to investigate the conformational differences between the native and the mutant proteins and found that the identified deleterious mutations alter the stability, flexibility, and solvent-accessible surface area of the protein. Furthermore, the functional role of each SNP in GCK was identified and characterised using SNPeffect 4.0, F-SNP, and FASTSNP. We hope that the observed results aid in the identification of disease-associated mutations that affect protein structure and function. Our in silico findings provide a new perspective on the role of GCK mutations in MODY2 from an evolution-based structure-centric point of view. The computational architecture described in this paper can be used to predict the most appropriate disease phenotypes for large-genome sequencing projects and to provide individualised drug therapy for complex diseases such as diabetes.

Genetic Epidemiology of Glucose-6-Phosphate Dehydrogenase Deficiency in the Arab World

“…True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) were calculated for each mutation by comparing the tool results and the prevalence, which was also used to calculate the above mentioned parameters for the 5 in silico tools. The reliability of the prediction can be determined by Matthews correlation coefficient (MCC), where a score of 1 indicates the best reliability, −1 indicates the worst reliability and a score near to 0 indicates that the prediction is a result of chance42. Based on the results obtained from the in silico tools, the mutations that could have an effect on the protein were selected.…”

Structural Analysis of G1691S Variant in the Human Filamin B Gene Responsible for Larsen Syndrome: A Comparative Computational Approach

“…B). The loss of hydrophobic and negatively charged interaction might lead to loss of thermodynamics stability [George et al, ; Doss et al, ]. This supports that the loss of hydrophobic residue (VAL90) and negatively charged residue (ASP91) might serve the reason for destabilization of protein due to G1691S.…”

“…The distinction between the variants which affect the protein structure and function from those who have little or no effect from larger pool of data obtained through experimental techniques is highly tedious [Doss et al, ]. The prioritization of pathogenic variants and studying their structural influence at molecular level through computational approaches has become the future step for drug therapeutic approach [George et al, ; Doss et al, ; Thirumal et al, ].…”

“…Using multiple in silico prediction tools could increase the reliability of the predictions of variant pathogenicity [Capriotti et al, ; Bendl et al, ; Doss et al, ]. These combinations were used in earlier studies to prove the potential impact of missense variants in other proteins such as Canavan disease, MODY2, PIK3CA, TITIN, and G6PD [George et al, ; Ali et al, ; Doss et al, ; Sudhakar et al, ; Thirumal et al, ; Zaki et al, ].…”

Prediction of functionally significant single nucleotide polymorphisms inPTENtumor suppressor gene: Anin silicoapproach

“…Furthermore, the SASA analysis of the mutant PTEN protein displayed notable fluctuations from the native PTEN protein structure. The increased solvent‐accessible area in the mutant protein structures indicates that the single nucleotide polymorphism causing residues changes in the protein structures may increase the probability of their interaction with surrounding molecules and vice versa . Thus, the trajectory analysis for the mutant structures G129E, C124R, D252G, H61D, and R130G revealed the extent of deleterious effects of single nucleotide polymorphisms influencing the functional and structural attributes of the protein.…”